mlsanityData to 14 Oct 2026

How to Ask the AI: A User Perspective Survey for Large Language Model Prompting

arXiv 2608.07494 ↗First posted 17 Jun 2026cs.HC cs.AIcited by 1

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Abstract (arXiv)

AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc., which are also known as LLM prompts. Crafting clear and well-structured prompts leads to more appropriate LLM feedback, which effectively bridges human-LLM interaction. Although prompting appears accessible to non-expert users, precisely organizing effective prompts is a highly systematic and skillful process, presenting potential challenges even for experienced users. This survey explores the principles, taxonomy, and organization of prompts from a user-centered perspective. Differing from the existing surveys that primarily focus on technical principles and application scenarios of LLMs, this paper provides actionable guidelines for formulating effective LLM prompts across diverse real-world tasks and specifically contributes by: 1) developing an intuitive evaluation strategy for prompt efficacy, 2) providing prompting workflow demonstrations on representative applications, and 3) maintaining a dynamically updated open-source project to ensure the core takeaways remain up-to-date. These measures lower the threshold for users to correctly understand and craft prompts that align with evolving application scenarios. This work will be maintained as a living GitHub project hrefhttps://github.com/Yunfan-Zhang/TAIGuideline-Tabletextcolorbluehere.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 72 of its 113 references that are in the map. First posted on arXiv 17 Jun 2026.

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72 of 113 references.

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38 more
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